In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature sel...

The aim of this study is to propose a suitable and reliable system for better diagnosis and treatment of carotid diseases. In this study, Computer Aided Diagnosis (CAD) system has been proposed for classifying carotid artery plaques using Contourlet features. Carotid images have been acquired for 124 subjects with symptoms (Amaurosis Fugax, Stroke or Transient Ischemic Attack) and 133 subjects ...

In this paper, we study the use of boosted weak classifiers selected with AdaBoost algorithm in object detection. Our work is motivated by the good performance of AdaBoost in selecting discriminative features and the effectiveness of Classification and Regression Tree (CART) compared with other classification methods. First, we study the cascaded structure of the boosted weak classifier detecto...

This paper presents a post-classification approach that can achieve efficient results for change detection by using AdaBoost classifier. In the first step, a land cover of satellite image is classified in the independent fashion. For the stable classification of man-made structures, 3D features are employed. 3D co-occurrence feature is used with 2D co-occurrence feature and Harr-like feature. A...

Hand gesture recognition is a topic in artificial intelligence and computer vision with the goal to automatically interpret human hand gestures via some algorithms. Notice that it is a difficult classification task for which only one simple classifier cannot achieve satisfactory performance; several classifier combination techniques are employed in this paper to handle this specific problem. Ba...

This paper presents a novel classification framework derived from AdaBoost to classify facial expressions. The proposed framework adopts rotation-reversal invariant HOG as features. The Framework is implemented through configuring the Area under ROC curve (AUC) of the weak classifier with HOG, which is a discriminative classification framework. The proposed classification framework is evaluated...

In this paper we present improved training algorithms to two newly developed classifiers, reduced set vector machines and Adaboost cascade classifier applied in face detection, which are all based on learning from data. Support vector machine (SVM) has been proved to be a powerful tool for solving practical pattern recognition problems based on learning from data. Due to large number of support...

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of squareblocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature sele...

We propose AdaBoost.BHC, a novel multi-class boosting algorithm. AdaBoost.BHC solves a C class problem by using C− 1 binary classifiers defined by a hierarchy that is learnt on the classes based on their closeness to one another. It then applies AdaBoost to each binary classifier. The proposed algorithm is empirically evaluated with other multi-class AdaBoost algorithms using a variety of datas...